Systems and methods for determining optimum patient-specific antibody dose for tumor targeting

ABSTRACT

The present disclosure describes a non-linear compartmental model using PET-derived data to predict, on a patient-specific basis, the optimal therapeutic dose of cargo carrying antibody (e.g., huA33) such as radiolabeled antibody, the antigen occupancy, residency times in normal and malignant tissues, and the cancer-to-normal tissue (e.g., colorectal cancer-to-normal colon tissue) therapeutic index. In addition, the non-linear compartmental model can be readily applied to the development of strategies such as multi-step targeting (MST) designed to further improve the therapeutic indices of RIT.

CROSS REFERENCE TO RELATED APPLICATION

This application is the U.S. National Stage Entry of International Application No. PCT/US16/33217 filed May 19, 2016, which claims the benefit of U.S. Application Serial No. 62/165,699 filed on May 22, 2015, the disclosures of which are hereby incorporated by reference in their entireties.

GOVERNMENT FUNDING

This invention was made with government support under Grant. No. R21 CA 153177-03 awarded by the National Cancer Institute (NCI) Center to Reduce Cancer Health Disparities; Grant. No. P50 CA086438 awarded by NCI; Grant Nos. PO1 CA 33049, R25-CA096945, R24-CA83084, P30-CA08748, and P50-CA92629 awarded by the National Institutes of Health (NIH). The government has certain rights in this invention.

FIELD OF THE INVENTION

This invention relates generally to non-invasive positron emission tomography (PET) imaging of targeted antibody radiotracer uptake and subsequent metabolism. In particular embodiments, the invention relates to a PET-based non-linear compartmental modeling for individualizing tumor targeting of antibodies.

BACKGROUND

Over the last decade, monoclonal antibodies (mAbs) as a class of drugs have achieved critical importance in oncology both clinically and commercially. It is projected, for example, that in the near future four of the five top oncology products in the world will be antibody-based pharmaceuticals, such as rituximab, trastuzumab, bevacizumab, cetuximab, other mAbs (e.g., immune effectors), and targeting vectors for therapeutic drugs and/or radioisotopes (e.g., ado-trastuzumab emtansine). Moreover, it is predicted that global sales will increase from $48 billion in 2008 to $75-80 billion in the near future. Nonetheless, despite the clinical and commercial importance of antibodies to oncology, factors which impact antibody in vivo biodistribution and their biologic effectiveness remain poorly understood. As a result, optimized dose and dosing scheduling continue to be determined empirically by laborious, time-consuming, and costly dose-escalation studies.

The molecular specificity of monoclonal antibodies directed against tumor antigens has proven effective for targeted therapy for human cancers. For example, clinical studies with murine anti-A33 mAb have demonstrated its safety, tumor-targeting ability, and specificity, even though development of human anti-mouse antibody (HAMA) was common and resulted in accelerated clearance of subsequent administrations of the mAb. Initial trials with huA33 alone or combined with chemotherapy have shown favorable tolerability, some evidence of therapeutic response, and less immunogenicity than murine anti-A33 mAb.

Prior work by the inventors determined the uptake and kinetics of an ¹²⁴I-labeled humanized anti-A33 mAb, huA33, in tumor and normal-tissue of colorectal cancer patients using positron emission tomography (PET) with tissue sampling at surgery (O'Donoghue J A, Smith-Jones P M, Humm J L, et al.: 124I-huA33 antibody uptake is driven by A33 antigen concentration in tissues from colorectal cancer patients imaged by immuno-PET. J Nucl Med. 52:1878-85, 2011; Carrasquillo J A, Pandit-Taskar N, O'Donoghue J A, et al.: (124)I-huA33 antibody PET of colorectal cancer. J Nucl Med. 52:1173-80, 2011, each of which is hereby incorporated by reference in its entirety).

It was determined that PET imaging can accurately and non-invasively quantitate the time-dependent antibody concentrations in vivo. Moreover, it was found that the “equilibrium” uptake of radiolabeled antibody (e.g., huA33) in antigen (e.g., A33)-expressing normal and tumor tissues was driven primarily by the antigen concentration and such uptake was saturable.

Current dosing models use linear models that are not patient-specific. However, because of the saturability of the antibody-antigen interaction described above, non-linear analysis would be required for patient-specific optimization of therapeutic antibody-targeting strategies. For example, the concentration of A33 in targeted tumors, the total mass of the A33-expressing tumor, and the total number of A33 binding sites can vary among patients. Moreover, the anti-A33 mAb-to-A33 exchange rate in vivo, the tumor concentration, and tumor-to-normal tissue ratios of the antibody also varies patient-to-patient. The latter parameters change as the dose of antibody (in moles) is varied. As a result, even if adjusted for body mass, body surface area, etc., there is no single optimum antibody dose which is translatable from patient to patient.

Therefore, there is a need for an approach that optimizes radioimmunotherapy (RIT) for individual patients. Individualizing tumor therapy requires administration of a pre-therapy tracer of the mAb to the patient, an imaging-based measurement of its time-activity data in blood, tumor, and antigen-expressing normal tissue(s), fitting of these data to the non-linear model, and model-based determination of the optimum antibody dose (in moles) for that patient.

SUMMARY OF INVENTION

Disclosed herein is a non-linear compartmental model using PET-derived data to determine the best-fit parameters and model-derived quantities for optimizing biodistribution of intravenously injected anti-tumor antibodies for patient-specific targeting in colorectal cancer. In certain embodiments, the disclosed model is based on the A33 antibody to improve radioimmunotherapy methodologies, which are subject to empirical determination of the optimized dose and dosing scheduling by laborious, time-consuming and costly dose-escalation studies. In certain embodiments, the disclosed model is based on the hu11B6 antibody to improve radioimmunotherapy methodologies, which are subject to empirical determination of the optimized dose and dosing scheduling by laborious, time-consuming and costly dose-escalation studies. In certain embodiments, the disclosed model is based on the J591 antibody to improve radioimmunotherapy methodologies, which are subject to empirical determination of the optimized dose and dosing scheduling by laborious, time-consuming and costly dose-escalation studies. In certain embodiments, the disclosed model is based on any antibody to improve radioimmunotherapy methodologies, which are subject to empirical determination of the optimized dose and dosing scheduling by laborious, time-consuming and costly dose-escalation studies.

The disclosed compartmental-modeling approach can be used to predict, on a patient-specific basis, the optimal therapeutic dose of a radiolabeled antibody, including but not limited to huA33, the antigen occupancy, residency times in normal and malignant tissues, and the cancer-to-normal tissue therapeutic index (e.g., colorectal cancer-to-normal colon therapeutic index). In addition, non-linear compartmental modeling can be readily applied to the development of strategies such as multi-step targeting (MST) designed to further improve the therapeutic indices of RIT. In certain embodiments, the disclosed non-linear compartmental modeling approach is used to predict tumor exposure of chemotherapies, toxins, and other cargos (e.g., immune effectors, fluorescent probes, porphyrins, radiographic contrast agents (e.g., metals, MRI agents)) that are transported and bound to an antibody.

In one aspect, the invention is directed to a method for determining a patient-specific optimum mAb dose, the method comprising: administering to a subject a radiolabeled monoclonal antibody (mAb) as pre-therapy tracer of the mAb; imaging the subject to obtain a plurality of sets of images acquired over a period of time, wherein each set of images is acquired at a particular time following administration of the radiolabeled monoclonal antibody, and wherein each set of images visualizes a blood pool, tumor, and antigen-positive normal tissue; determining, by a processor of a computing device, from the plurality of sets of images, measurements of time-activity of the pre-therapy tracer of the mAb in blood, tumor, and antigen-expressing normal tissue for the subject; identifying, by the processor, one or more parameters of a nonlinear compartmental model of the kinetics/biodistribution of the pre-therapy tracer in the subject from the determined measurements of time-activity for the subject; and determining, by the processor, a patient-specific optimal therapeutic antibody dose (or multiple doses) for the subject using the nonlinear compartmental model with the identified one or more parameters.

In certain embodiments, the monoclonal antibody is selected from the group consisting of A33 antibody, hu11B6 antibody, J591 antibody, trastuzumab, MSTP2109A, Df-IAB2M, cmAb-U36, ibritumomab tiuxetan, bevacizumab, rituximab, any antibody targeting human epidermal growth factor receptor 3 (e.g., GSK2849330), and any antibody targeting human carcinoembryonic antigen (e.g., AMG11).

In certain embodiments, the radiolabel is selected from the group consisting of ⁹⁹Tc, ¹¹¹In, ⁶⁴Cu, ⁶⁷Ga, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁷⁷Lu, ⁶⁷Cu, ¹²³I, ¹²⁴I, ¹²⁵I, ¹¹C, ¹3N, ¹⁵O, ¹⁸F, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁶⁶Ho, ¹⁷⁷Lu, ¹⁴⁹Pm, ⁹⁰Y, ²¹³Bi, ¹⁰³Pd, ¹⁰⁹Pd, ¹⁵⁹Gd, ¹⁴⁰La, ¹⁹⁸Au, ¹⁹⁹Au, ¹⁶⁹Yb, ¹⁷⁵Yb, ¹⁶⁵Dy, ¹⁶⁶Dy, ⁶⁷Cu, ¹⁰⁵Rh, ¹¹¹Ag, ⁸⁹Zr, ²²⁵Ac, ¹³¹I, ²¹²B, and ¹⁹²Ir. In certain embodiments, the radiolabel comprises ⁸⁹Zr. In certain embodiments, the radiolabel comprises ²²⁵Ac. In certain embodiments, the radiolabel comprises ¹²⁴I.

In certain embodiments, the method further comprises acquiring the plurality of sets of images via positron emission tomography (PET).

In certain embodiments, a first set of the images is acquired several hours after antibody administration and at least a second set of the images is acquired up to 2 weeks after antibody administration.

In certain embodiments, the first set of images is acquired at least 2 hours, at least 3 hours, at least 4 hours, or at least 5 hours after antibody administration.

In certain embodiments, the antigen-positive normal tissue comprises colon tissue (e.g., wherein the monoclonal antibody is A33 antibody). In certain embodiments, the antigen-positive normal tissue comprises breast tissue (e.g., wherein the monoclonal antibody is hu11B6 antibody). In certain embodiments, the antigen-positive normal tissues comprise prostate tissue (e.g., wherein the monoclonal antibody is J591 antibody).

In certain embodiments, the therapeutic antibody is the same monoclonal antibody as in the pre-therapy tracer. In certain embodiments, the therapeutic antibody is different than the monoclonal antibody in the pre-therapy tracer.

In certain embodiments, the measurements of time-activity of the pre-therapy tracer comprise one or more members selected from the group consisting of a mean activity concentration of the radiolabeled antibody in the blood, a mean activity concentration of the radiolabeled antibody in the tumor, and a mean activity concentration of the radiolabeled antibody in the antigen-expressing normal tissue.

In certain embodiments, the antigen-expressing normal tissue comprises a member selected from the group consisting of bowel tissue, breast tissue, prostate tissue, kidney, liver, salivary gland, and lung.

In certain embodiments, the method further comprises imaging the subject via computed tomography (CT) or magnetic resonance imaging (MRI) to determine a tumor mass.

In certain embodiments, a mean activity concentration of the radiolabeled antibody in the tumor is determined, by the processor, from a total activity concentration by multiplying the tumor mass by the total activity concentration and an elapsed time after the administering step.

In certain embodiments, the radiolabeled mAb comprises a radiolabel with a half-life compatible with imaging-based activity concentration measurements in tumor and other tissues.

In certain embodiments, the half-life is consistent with the turnover rate of intestinal mucosa in the case of A33 mAb.

In certain embodiments, the radiolabeled mAb is administered over a period of time from 5 minutes to 20 minutes.

In certain embodiments, the administering step comprises administering two or more doses of the pre-therapy tracer at different times, and wherein the method further comprises determining, by the processor, one or more optimum times of administration of the pre-therapy tracer (non-radiolabeled mAb).

In certain embodiments, the pre-therapy tracer comprises a bi-specific tetravalent antibody/-[metal-DOTA] antibody construct (nALDO, or huA33-C825) which reacts with an antigen and with radiometal-labeled DOTA-Bn (DOTA-Bn: (2-(4-aminobenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl) tetraacetic acid).

In certain embodiments, the antibody is a member selected from the group consisting of A33 antibody, hu11B6 antibody, and J591 antibody and the antigen is a member selected from the group consisting of A33, hu11B6, and J591.

In certain embodiments, the nonlinear compartmental model comprises one or more of the following assumptions: (i) no free radioiodine or radioiodinated metabolites of the radiolabeled mAb are produced in vivo; (ii) there is no significant amount of free (non-bound) radiolabeled mAb in normal tissue and in tumor tissue; (iii) there is no significant dissociation of the bound radiolabeled mAb from the antigen; and (iv) the mAb-antigen association rate constant (ka) is the same for antigen in normal tissue and in tumor.

In certain embodiments, the radiolabeled mAb is a member selected from the group consisting of ¹²⁴I-labeled anti-A33, ⁸⁹Zr-labeled anti-hu11B6, ⁸⁹Zr-labeled anti-J591, ⁸⁹Zr-trastuzumab, ⁸⁹Zr-MSTP2109A, ⁸⁹Zr-Df-IAB2M, ⁸⁹Zr-cmAb-U36, ⁸⁹Zr-ibritumomab tiuxetan, ⁸⁹Zr-bevacizumab, and ⁸⁹Zr-rituximab.

In certain embodiments, the antigen is a member selected from the group consisting of A33, hu11B6, J591, GPA33, kallikrein hk2, and prostate specific membrane (PSMA).

In certain embodiments, the tissue (e.g., any diseased tissue) is a member selected from the group consisting of bowel tissue, breast tissue, prostate tissue, kidney, liver, salivary gland, and lung.

In certain embodiments, the method comprises determining, by the processor, a quantification of internalization and turnover of an antibody-cargo via the nonlinear compartmental model.

In certain embodiments, the antibody-cargo comprises a member selected from the group consisting of a radiolabel, anti-cancer drug, immune effectors, fluorescent probes, porphyrins, radiographic contrast agents (e.g., metals, MRI agents), and cytotoxin.

In certain embodiments, the anti-cancer drug comprises a member selected from the group consisting of calicheamicin, doxirubicin, dolstatin/auristatin, maytansine, emtansine, ravtansine, alpha amanitin, pyrolobenzodiazapine, tubulysins, rituximab, trastuzumab, bevacizumab, anti-huJ591, MSTP2109A, Df-IAB2M, cmAb-U36, ibritumomab tiuxetan, bevacizumab, rituximab, any antibody targeting human epidermal growth factor receptor 3 (e.g., GSK2849330), and any antibody targeting human carcinoembryonic antigen (e.g., AMG11), dasatinib and other pathway inhibitors, bevatuzimab, cetuximab, and at least one drug depicted in Table 2.

In certain embodiments, the radiolabel comprises a member selected from the group consisting of ²²⁵Ac, ¹⁷⁷Lu, ²¹¹At, ¹³¹I, ¹²⁴I, ⁹⁰Y, ⁶⁴Cu, ⁶⁸Ga, ⁶⁶Ga, and ⁶⁷Cu.

In certain embodiments, the nonlinear compartmental model comprises a representation of an interaction of the pre-therapy tracer of the mAb with tissue.

In certain embodiments, the interaction comprises a member selected from the group consisting of targeting antibody affinity, antigen density, and internalization rates of antibody-antigen complex.

In certain embodiments, the nonlinear compartmental model is as depicted in FIG. 2.

In certain embodiments, the administering step comprises administering a monoclonal antibody with a first cargo and a second cargo, the method further comprising estimating, by the processor, a residence time of the first and second antibody cargos associated with the same (type of) mAb.

In certain embodiments, the method further comprises administering the therapeutic antibody dose (or multiple doses) to the subject.

In certain embodiments, the administration is via injection.

In certain embodiments, the one or more parameters comprises a targeting index.

In certain embodiments, the targeting index comprises a tumor-to-plasma ratio of integrals of tumor and plasma activity concentrations.

In certain embodiments, the one or more parameters comprises a therapeutic index.

In certain embodiments, the therapeutic index comprises a PET image-derived tumor-to-normal tissue ratio (e.g., area-under-the-curve (AUC) concentration (% ID/g·h) ratio).

In certain embodiments, the tumor-to-normal tissue comprises a member selected from the group consisting of tumor-to-bowel mucosa, tumor-to-breast, tumor-to-prostate, tumor-to-lung, tumor-to-kidney, tumor-to-liver, tumor-to-salivary gland, tumor-to-bone marrow, tumor-to-liver, tumor-to-lung, tumor-to-kidney, tumor-to-bowel, tumor-to-bladder, tumor-to-gonads, and tumor-to-brain.

In another aspect, the invention is directed to a method of determining a patient-specific optimum mAb dose, the method comprising: receiving, by a processor of a computing device, a plurality of sets of images acquired over a period of time, wherein each set of images is acquired at a particular time following administration of the radiolabeled monoclonal antibody, and wherein each set of images visualizes a blood pool, tumor, and antigen-positive normal tissue; determining, by the processor, from the plurality of sets of images, measurements of time-activity of the pre-therapy tracer of the mAb in blood, tumor, and antigen-expressing normal tissue for the subject; identifying, by the processor, one or more parameters of a nonlinear compartmental model of the kinetics/biodistribution of the pre-therapy tracer in the subject from the determined measurements of time-activity for the subject; and determining, by the processor, an optimum therapeutic antibody dose (or multiple doses) for the subject using the nonlinear compartmental model with the identified one or more parameters.

In certain embodiments, the method further comprises acquiring the plurality of sets of images via positron emission tomography (PET).

In certain embodiments, a first set of the images is acquired several hours after post-antibody administration and at least a second set of the images is acquired up to 2 weeks after antibody administration.

In certain embodiments, the first set of images is acquired at least 2 hours, at least 3 hours, at least 4 hours, or at least 5 hours after post-antibody administration.

In certain embodiments, the antigen-positive normal tissue comprises colon tissue (e.g., wherein the radiolabeled monoclonal antibody comprises A33 antibody). In certain embodiments, the antigen-positive normal tissue comprises breast tissue (e.g., wherein the radiolabeled monoclonal antibody comprises hu11B6 antibody). In certain embodiments, the antigen-positive normal tissue comprises prostate tissue (e.g., wherein the radiolabeled monoclonal antibody comprises J591 antibody).

In certain embodiments, the therapeutic antibody is the same monoclonal antibody as in the pre-therapy tracer. In certain embodiments, the therapeutic antibody is different than the monoclonal antibody in the pre-therapy tracer.

In certain embodiments, the one or more parameters comprise a targeting index.

In certain embodiments, the targeting index comprises a tumor-to-plasma ratio of integrals of tumor and plasma activity concentrations.

In certain embodiments, the one or more parameters comprises a therapeutic index.

Elements of embodiments involving one aspect of the invention (e.g., methods) can be applied in embodiments involving other aspects of the invention (e.g., systems), and vice versa.

Definitions

In order for the present disclosure to be more readily understood, certain terms are first defined below. Additional definitions for the following terms and other terms are set forth throughout the specification.

In this application, the use of “or” means “and/or” unless stated otherwise. As used in this application, the term “comprise” and variations of the term, such as “comprising” and “comprises,” are not intended to exclude other additives, components, integers or steps. As used in this application, the terms “about” and “approximately” are used as equivalents. Any numerals used in this application with or without about/approximately are meant to cover any normal fluctuations appreciated by one of ordinary skill in the relevant art. In certain embodiments, the term “approximately” or “about” refers to a range of values that fall within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in either direction (greater than or less than) of the stated reference value unless otherwise stated or otherwise evident from the context (except where such number would exceed 100% of a possible value).

“Administration”: The term “administration” refers to introducing a substance into a subject. In general, any route of administration may be utilized including, for example, parenteral (e.g., intravenous), oral, topical, subcutaneous, peritoneal, intraarterial, inhalation, vaginal, rectal, nasal, introduction into the cerebrospinal fluid, or instillation into body compartments. In some embodiments, administration is oral. Additionally or alternatively, in some embodiments, administration is parenteral. In some embodiments, administration is intravenous.

“Approximately”: As used herein, the term “approximately” or “about,” as applied to one or more values of interest, refers to a value that is similar to a stated reference value. In certain embodiments, the term “approximately” or “about” refers to a range of values that fall within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in either direction (greater than or less than) of the stated reference value unless otherwise stated or otherwise evident from the context (except where such number would exceed 100% of a possible value).

“Biocompatible”: The term “biocompatible”, as used herein is intended to describe materials that do not elicit a substantial detrimental response in vivo. In certain embodiments, the materials are “biocompatible” if they are not toxic to cells. In certain embodiments, materials are “biocompatible” if their addition to cells in vitro results in less than or equal to 20% cell death, and/or their administration in vivo does not induce inflammation or other such adverse effects. In certain embodiments, materials are biodegradable.

“Biodegradable”: As used herein, “biodegradable” materials are those that, when introduced into cells, are broken down by cellular machinery (e.g., enzymatic degradation) or by hydrolysis into components that cells can either reuse or dispose of without significant toxic effects on the cells. In certain embodiments, components generated by breakdown of a biodegradable material do not induce inflammation and/or other adverse effects in vivo. In some embodiments, biodegradable materials are enzymatically broken down. Alternatively or additionally, in some embodiments, biodegradable materials are broken down by hydrolysis. In some embodiments, biodegradable polymeric materials break down into their component polymers. In some embodiments, breakdown of biodegradable materials (including, for example, biodegradable polymeric materials) includes hydrolysis of ester bonds. In some embodiments, breakdown of materials (including, for example, biodegradable polymeric materials) includes cleavage of urethane linkages.

“Carrier”: As used herein, “carrier” refers to a diluent, adjuvant, excipient, or vehicle with which the compound is administered. Such pharmaceutical carriers can be sterile liquids, such as water and oils, including those of petroleum, animal, vegetable or synthetic origin, such as peanut oil, soybean oil, mineral oil, sesame oil and the like. Water or aqueous solution saline solutions and aqueous dextrose and glycerol solutions are preferably employed as carriers, particularly for injectable solutions. Suitable pharmaceutical carriers are described in “Remington's Pharmaceutical Sciences” by E. W. Martin.

“Excipient”: As used herein, the term “excipient” refers to a non-therapeutic agent that may be included in a pharmaceutical composition, for example to provide or contribute to a desired consistency or stabilizing effect. Suitable pharmaceutical excipients include, for example, starch, glucose, lactose, sucrose, gelatin, malt, rice, flour, chalk, silica gel, sodium stearate, glycerol monostearate, talc, sodium chloride, dried skim milk, glycerol, propylene, glycol, water, ethanol and the like.

“Image”: As used herein, an “image” includes any visual representation, such as a photo, a video frame, streaming video, as well as any electronic, digital or mathematical analogue of a photo, video frame, or streaming video. Any apparatus described herein, in certain embodiments, includes a display for displaying an image or any other result produced by the processor. Any method described herein, in certain embodiments, includes a step of displaying an image or any other result produced via the method. An image may be rendered as a dataset in two or three dimensions and/or may be displayed as one or more two-dimensional representations, or as a three-dimensional representation.

“Radiolabel”: As used herein, “radiolabel” refers to a moiety comprising a radioactive isotope of at least one element. Exemplary suitable radiolabels include but are not limited to those described herein. In some embodiments, a radiolabel is one used in positron emission tomography (PET). In some embodiments, a radiolabel is one used in single-photon emission computed tomography (SPECT). In some embodiments, radioisotopes comprise ^(99m)Tc, ¹¹¹In, ⁶⁴Cu, ⁶⁷Ga, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁷⁷Lu, ⁶⁷Cu, ¹²³I, ¹²⁴I, ¹²⁵I, ¹¹C, ¹³N, ¹⁵O, ¹⁸F, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁶⁶Ho, ¹⁷⁷Lu, ¹⁴⁹Pm, ⁹⁰Y, ²¹³Bi, ¹⁰³Pd, ¹⁰⁹Pd, ¹⁵⁹Gd, ¹⁴⁰La, ¹⁹⁸Au, ¹⁹⁹Au, ¹⁶⁹Yb, ¹⁷⁵Yb, ¹⁶⁵Dy, ¹⁶⁶Dy, ⁶⁷Cu, ¹⁰⁵Rh, ¹¹¹Ag, ⁸⁹Zr, ²²⁵Ac, ²¹¹At, ¹³¹I, ¹³¹I, ²¹²B, and ¹⁹²Ir.

“Substantially: As used herein, the term “substantially”, and grammatical equivalents, refers to the qualitative condition of exhibiting total or near-total extent or degree of a characteristic or property of interest. One of ordinary skill in the art will understand that biological and chemical phenomena rarely, if ever, go to completion and/or proceed to completeness or achieve or avoid an absolute result.

“Subject”: As used herein, the term “subject” includes humans and mammals (e.g., mice, rats, pigs, cats, dogs, and horses). In many embodiments, subjects are mammals, particularly primates, especially humans. In some embodiments, subjects are livestock such as cattle, sheep, goats, cows, swine, and the like; poultry such as chickens, ducks, geese, turkeys, and the like; and domesticated animals particularly pets such as dogs and cats. In some embodiments (e.g., particularly in research contexts) subject mammals will be, for example, rodents (e.g., mice, rats, hamsters), rabbits, primates, or swine such as inbred pigs and the like.

“Therapeutic agent”: As used herein, the phrase “therapeutic agent” refers to any agent that has a therapeutic effect and/or elicits a desired biological and/or pharmacological effect, when administered to a subject.

“Treatment”: As used herein, the term “treatment” (also “treat” or “treating”) refers to any administration of a substance that partially or completely alleviates, ameliorates, relives, inhibits, delays onset of, reduces severity of, and/or reduces incidence of one or more symptoms, features, and/or causes of a particular disease, disorder, and/or condition. Such treatment may be of a subject who does not exhibit signs of the relevant disease, disorder and/or condition and/or of a subject who exhibits only early signs of the disease, disorder, and/or condition. Alternatively or additionally, such treatment may be of a subject who exhibits one or more established signs of the relevant disease, disorder and/or condition. In some embodiments, treatment may be of a subject who has been diagnosed as suffering from the relevant disease, disorder, and/or condition. In some embodiments, treatment may be of a subject known to have one or more susceptibility factors that are statistically correlated with increased risk of development of the relevant disease, disorder, and/or condition.

Drawings are presented herein for illustration purposes, not for limitation.

BRIEF DESCRIPTION OF DRAWINGS

The foregoing and other objects, aspects, features, and advantages of the present disclosure will become more apparent and better understood by referring to the following description taken in conduction with the accompanying drawings, in which:

FIG. 1 shows coronal PET images of a patient with colorectal cancer metastatic to liver at 2 days and 7 days after injection of ¹²⁴I-huA33 mAb. The images shown are maximum-intensity projections (MIPs). The 2-day image shows two high-uptake foci in the liver (single arrow) and splenic flexure (double arrow) corresponding to known metastatic lesions and some uptake in bowel. At 7 days, there is persistent uptake in the liver metastases and prominent uptake in normal bowel (triple arrow). For this patient, the model-derived A33 concentrations in tumor and normal bowel were 500 nM and 20 nM, respectively, and the maximum A33 occupancies 10% and 1%, respectively. These occupancy values are at the low end of the range for the patient cohort as shown in Table 1.

FIG. 2 shows a non-linear compartmental model of systemically administered anti-A33 mAb in patient with A33-expressing tumors. The square brackets indicate concentrations (in M), the indices 1, 2, and 3 correspond to plasma, normal bowel, and tumor, respectively (as indicated), k(i,j) is the fractional exchange rate of anti-A33 mAb to compartment i from compartment j (in/h), Q_(i) is the amount of anti-A33 mAb in compartment i (in mole), Vi is the volume of compartment i (in 1) (equivalent to the mass of tissue i in kg), k(0,i) is the rate of elimination of ¹²⁴I-mAb from compartment i (in/h), Flux (i,j) is the absolute exchange rate of anti-A33 mAb to compartment i from compartment j (in mole/h), and ka is the association rate constant for the binding of the anti-A33 mAb to A33 (in/M/h). Evaluation of the values of the various “k” parameters in the model for specific patients is performed by computerized fitting of the model to the patient's measured time-activity data, that is, solving of the system of differential equations in FIG. 2.

FIG. 3 shows time-activity data % ID/g versus time post-injection) for plasma, A33-expressing colorectal tumor, and normal colon following intravenous injection of ¹²⁴I-A33 into a colorectal cancer patient. The points represent the measured data and the curves the “best-fit” model-derived data obtained using the model in FIG. 2. For this case, the best-fit model-derived tumor and normal-colon A33 concentrations were 2.0 nM and 4.5 nM, respectively, and the antibody-antigen association rate constant 6.5×10⁻⁹/M/hr.

FIG. 4 is a block diagram of an example network environment for use in the methods and systems for analysis of spectrometry data, according to an illustrative embodiment.

FIG. 5 is a block diagram of an example computing device and an example mobile computing device, for use in illustrative embodiments of the invention.

FIG. 6 shows that the biodistributions (or the percent of the administered activity per gram of tissue, % IA/g) at 244 hours post-administration of ⁸⁹Zr-DFO-hu11B6 and ²²⁵Ac-DOTA-hu11B6 antibody targeting breast cancer in mice bearing human BT474 breast tumor xenografts are shown, within experimental error, to be identical (lg-int: large intestine; sm-int: small intestine).

FIG. 7 show an embodiment of the non-linear compartmental model applied to a quantitative comparison of absolute uptake and antibody turnover in prostate cancer (PCa) of the anti-prostate-specific membrane antigen (PSMA) antibody J591 labeled with ⁸⁹Zr (or ⁸⁹Zr-J591).

DETAILED DESCRIPTION

Throughout the description, where compositions are described as having, including, or comprising specific components, or where methods are described as having, including, or comprising specific steps, it is contemplated that, additionally, there are compositions of the present invention that consist essentially of, or consist of, the recited components, and that there are methods according to the present invention that consist essentially of, or consist of, the recited processing steps.

It should be understood that the order of steps or order for performing certain action is immaterial so long as the invention remains operable. Moreover, two or more steps or actions may be conducted simultaneously.

The mention herein of any publication, for example, in the Background section, is not an admission that the publication serves as prior art with respect to any of the claims presented herein. The Background section is presented for purposes of clarity and is not meant as a description of prior art with respect to any claim.

The molecular specificity of monoclonal antibodies (mAbs) directed against tumor antigens has proven effective for targeted therapy of human cancers, as witnessed by a growing list of successful antibody-based drug products.

Described herein is a non-linear compartmental model using positron emission tomography (PET)-derived data to determine the “best-fit” parameters and model-derived quantities for optimizing biodistribution of intravenously injected radiolabeled-labeled anti-tumor antibodies. As an example of this paradigm, quantitative image and kinetic analyses of anti-A33 humanized mAb (also known as “A33”) were performed in colorectal cancer patients (n=11). Serial whole-body PET scans of ¹²⁴I-labeled A33 and blood samples were acquired, and the resulting tissue time-activity data for each patient were fit to a non-linear compartmental model using the SAAM II (Simulation, Analysis, and Modeling) computer code. Agreement was observed between fitted and measured parameters of tumor uptake, “off-target” uptake in bowel mucosa, blood clearance, tumor antigen levels, and percent antigen occupancy.

This approach was applied to, but is not limited to, various antibody-antigen systems in human tumors for which the masses of antigen-expressing tumor and normal tissues were estimated. Moreover, this approach was used to measure antibody kinetics with PET. Thus, this approach can enable a patient-specific optimum mAb dose (in μmol, for example) to be derived based on each patient's resulting “best-fit” non-linear model.

In certain embodiments, the methodology described is applicable to multi-step targeting strategies wherein a non-radiolabeled mAb is first administered followed by a radiolabeled reagent which cross-reacts with that mAb as well as a directly radiolabeled mAb.

In certain embodiments, the radiolabel is monitored and/or imaged (e.g., via PET or single-photon emission computed tomography (SPECT)). Example radiolabels that can be used include beta emitters (e.g., ¹⁷⁷Luteium) and alpha emitters (e.g., ²²⁵Ac) In certain embodiments, one or more of the following radioisotopes are used: ^(99m)Tc, ¹¹¹In, ⁶⁴Cu, ⁶⁷Ga, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁷⁷Lu, ⁶⁷Cu, ¹²³I, ¹²⁴I, ¹²⁵I, ¹¹C, ¹3N, ¹⁵O, ¹⁸F, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁶⁶Ho, ¹⁷⁷Lu, ¹⁴⁹Pm, ⁹⁰Y, ²¹³Bi, ¹⁰³Pd, ¹⁰⁹Pd, ¹⁵⁹Gd, ¹⁴⁰La, ¹⁹⁸Au, ¹⁹⁹Au, ¹⁶⁹Yb, ¹⁷⁵Yb, ¹⁶⁵Dy, ¹⁶⁶Dy, ⁶⁷Cu, ¹⁰⁵Rh, ¹¹¹Ag, ⁸⁹Zr, ²²⁵Ac, ¹³¹I, ²¹²B, and ¹⁹²Ir.

In certain embodiments, a chelator, for example, 1,4,8,11-tetraazabicyclo[6.6.2]hexadecane-4,11-diyl)diacetic acid (CB-TE2A); desferoxamine (DFO); diethylenetriaminepentaacetic acid (DTPA); 1,4,7,10-tetraazacyclotetradecane-1,4,7,10-tetraacetic acid (DOTA); ethylene diamine tetraacetic acid (EDTA); ethylene glycolbis(2-aminoethyl)-N,N,N′,N′-tetraacetic acid (EGTA); 1,4,8,11-tetraazacyclotetradecane-1,4,8,11-tetraacetic acid (TETA); ethylenebis-(2-4 hydroxy-phenylglycine) (EHPG); 5-Cl-EHPG; 5Br-EHPG; 5-Me-EHPG; 5t-Bu-EHPG; 5-sec-Bu-EHPG; benzodiethylenetriamine pentaacetic acid (benzo-DTPA); dibenzo-DTPA; phenyl-DTPA, diphenyl-DTPA; benzyl-DTPA; dibenzyl DTPA; bis-2 (hydroxybenzyl)-ethylene-diaminediacetic acid (HBED) and derivatives thereof; Ac-DOTA; benzo-DOTA; dibenzo-DOTA; 1,4,7-triazacyclononane N,N′,N″-triacetic acid (NOTA); benzo-NOTA; benzo-TETA, benzo-DOTMA, where DOTMA is 1,4,7,10-tetraazacyclotetradecane-1,4,7,10-tetra(methyl tetraacetic acid), benzo-TETMA, where TETMA is 1,4,8,11-tetraazacyclotetradecane-1,4,8,11-(methyl tetraacetic acid); derivatives of 1,3-propylenediaminetetraacetic acid (PDTA); triethylenetetraaminehexaacetic acid (TTHA); derivatives of 1,5,10-N,N′,N″-tris(2,3-dihydroxybenzoyl)-tricatecholate (LICAM); and 1,3,5-N,N′,N″-tris(2,3-dihydroxybenzoyl)aminomethylbenzene (MECAM), or other metal chelators, is used.

In certain embodiments the radioisotope-chelator pair is ⁸⁹Zr-DFO. In certain embodiments, the radioisotope-chelator pair is ²²⁵Ac-DOTA. In certain embodiments, the radioisotope-chelator pair is ¹⁷⁷Lu-DOTA. In certain embodiments, the radioisotope-chelator pair is ⁶⁸Ga-DOTA. In certain embodiments, other diagnostic, therapeutic, and/or theranostic radioisotope-chelators are used.

Described herein is a PET-based kinetic model which provides key parameters regarding in vivo uptake in tumor and normal tissue. As described above, the saturability of the antibody-antigen interaction requires non-linear analysis for patient-specific optimization of therapeutic antibody-targeting strategies. As an example, described herein is the development and initial validation of a non-linear compartmental model of the in vivo biodistribution and kinetics of huA33 based on PET imaging of ¹²⁴I-huA33. Other examples using various antibodies are included to further validate the disclosed non-linear compartmental model.

Routinely, radiolabeled mAbs are administered to patients at a single default dosage (e.g., such as 10 mg). However, the patient-specific optimum dosage (e.g., the dosage which maximizes uptake in a targeted tumor and minimizes uptake in non-targeted normal organs) can deviate widely among patients (perhaps over several orders of magnitude) from this default value, depending on such patient-dependent parameters as the patient's body weight, the mass of the tumor, and the concentration of the targeted antigen in the tumor, among many other parameters.

Serial non-invasive PET imaging of targeted antibody radiotracer uptake and subsequent metabolism (e.g., efflux from antigen-positive site) was utilized in conjunction with estimates of biological features of the radiolabeled target antigen interaction (e.g., targeting antibody affinity, antigen density, internalization rates of antibody-antigen complex) to quantify internalization and turnover of an antibody-cargo (e.g., wherein the cargo comprises a radiolabel, cytotoxin, immune effectors, fluorescent probes, and/or porphyrins) using non-linear compartmental modeling. This improves current radioimmunotherapy (RIT) and antibody-cytotoxic drug conjugates practices because the tumor-residence of the diagnostic radioisotope-antibody and subsequent kinetic modeling can be used to estimate the residence times of alternative cargos associated with the same targeting antibody or to evaluate the potential impact of increasing the dose of targeting antibody on the cargo uptake and residence time.

Two clinically utilized long-lived positron-emitting isotopes, for example, ⁸⁹Zr and ¹²⁴I have differences in biologic fate following intracellular uptake, such as “residualizing” and “non-residualizing”, respectively. Pairwise evaluation of radiolabeled variants (such as “residualizing” and “non-residualizing”, respectively) of the same targeting antibody in pre-clinical animal models can be useful to estimate differential response of tracer metabolism and quantitate turnover of bound antibody, thus guiding personalized treatment in patients.

As an example, the choice of A33 antigen as the in vivo target was based on features of the antigen-antibody interaction. Other antigens can be used. The expression of the A33 transmembrane glycoprotein is restricted to normal human bowel and colon (95%), gastric (95%), and pancreatic (50%) cancers. Upon binding of anti-A33 mAb, the resulting non-internalized antibody-antigen complex persists on the cell surface for several weeks. This membrane residence time is lengthy compared to other antibody-antigen complexes, and makes anti-A33 mAb particularly attractive for application to RIT. In contrast, turnover of A33-expressing cells in normal bowel is rapid, occurring every 5-6 days by exfoliation of mucosal cells into the bowel lumen. This combination of long persistence of antigen on the surface of tumor cells, a significant “off-target” antigen pool in the colon, the high percent uptake in tumor, and demonstrated safety and tolerability of multiple administrations in humans made the A33 antigen-antibody system a suitable model for initial studies with the PET-based non-linear kinetic modeling approach. Other antigen-antibody systems are also described herein.

The compartmental-modeling approach described herein can be used to predict, on a patient-specific basis, the optimal therapeutic dose of radiolabeled antibodies (e.g., huA33) as well as the antigen occupancy, residency times in normal and malignant tissues, and the cancer-to-normal tissue (e.g., colorectal) therapeutic index. In certain embodiments, a therapeutic index is defined as the PET image-derived tumor-to-normal tissue (e.g., bowel mucosa, breast, prostate, lung, kidney, liver, salivary gland) cumulated activity concentration (% ID/g·h) ratio.

Moreover, this non-linear compartmental model can be readily applied to the development of strategies such as multi-step targeting (MST) designed to further improve the therapeutic indices of RIT. This non-linear compartmental model is not limited to radiolabeled huA33, but also can be applied to various radiolabeled antibodies, including but not limited, hu11B6 and J591, and other cargos (e.g., toxins, immune effectors, fluorescent probes, porphyrins, radiographic contrast agents (e.g., metals, MRI agents)) as described herein.

In the context of the a MST strategies, there are three general steps: administration of the non-radioactive antibody which specifically targets and localizes in the tumor; a clearing agent, which is a non-radioactive reagent (e.g., streptavidin) which binds to any antibody still in circulations and thereby accelerates its clearance from blood; and finally a small radioactive carrier molecule which is either rapidly cleared from the circulation (by virtue of its small size) or binds to the antibody already localized in the tumor. In this way, delivery of radioactivity and therefore the radiation dose to all normal tissues (e.g., all tissues other than tumor) are minimized by delivery of radioactivity and radiation dose to tumor is maximized.

In certain embodiments, MST strategies, a non-radiolabeled mAb is first administered followed by a radiolabeled reagent which cross-reacts with that mAb as well as a directly radiolabeled mAb.

EXPERIMENTAL EXAMPLES Example 1: Non-Linear Compartmental Modeling of Radiolabeled Anti A33 mAb

For the current analysis, serial blood sampling, plasma counting, and whole-body PET imaging were performed up to about 1 week following intravenous injection of 10 mg of the humanized anti-A33 mAb (huA33) labeled with 10 mCi of ¹²⁴I into each of 11 colorectal cancer patients. The resulting kinetic data for each patient were fit to a compartmental model using the SAAM II (Simulation, Analysis, and Modeling) program as is shown in FIG. 2. FIG. 3 shows that the model-predicted and measured time-activity data for one patient in the current cohort. For example, the non-linear compartmental analysis yielded a goodness of fit of the model to the measured time-activity data; the model's goodness of fit to this patient's kinetic data is typical of that for all 11 patients analyzed. For 10 of the 11 patients, the Z-statistic indicated that the model fits were statistically acceptable at a 5% significance level. The validity of this model is further corroborated by the agreement between the antigen concentration in tumor derived from the model and from in vitro binding studies of cell membrane fractions obtained from resected tumor specimens as described by O'Donoghue et al., 2011. Among the three patients in the cohort for whom the latter analysis was performed, the model-derived and average binding study-derived antigen concentrations were 30 nM and 45 nM, 45 nM and 54 nM, and 15 nM and 6.1 nM. The “best-fit” model parameters for the 11-patient cohort are summarized in Table 1.

In contrast to typical, “non-saturable” kinetics, the model presented in FIG. 2 is non-linear. As a result of the saturable binding of the anti-A33 mAb to the A33 antigenic sites that are generally characteristic of antibody-antigen interactions, the exchange rate for the binding of the mAb to antigen progressively decreases as increasing numbers of antigenic sites are occupied by the antibody. The practical implications of the non-linearity of antibody-antigen interactions for immune targeting and the ability to predictively model these interactions on an individual-patient basis are important. For example, the concentration of A33 in targeted tumors, the total mass of the A33-expressing tumor, and the total number of A33 binding sites may vary widely among patients. Moreover, the anti-A33 mAb-to-A33 exchange rate in vivo, the tumor concentration, and tumor-to-normal tissue ratios of the antibody also varies. Variation also occurs because the dose of antibody (in moles). As a result, there is no single optimum antibody dose which is translatable from patient to patient, even adjusted for body mass, body surface area, etc. Rather, a more rational approach to optimizing RIT requires administration of a pre-therapy tracer of the mAb to the patient, an imaging-based measurement of its time-activity data in blood, tumor, and antigen-expressing normal tissue(s), fitting of these data to the non-linear model, and model-based determination of the optimum antibody dose (in moles) for that patient. The patient-specific optimum antibody dose for MT is that which yields the maximum tumor-to-normal tissue area-under-the-curve (AUC) concentration ratios and thus the tumor-to-normal tissue therapeutic index. For example, as shown in FIG. 2, computer simulations using the “best-fit” model parameters for the patients whose images are shown in FIG. 1 indicate that tumor targeting of huA33 mAb, expressed as the tumor-to-normal colon and tumor-to-plasma AUC concentration ratios, can be nearly doubled by increasing the administered dose of antibody from 10 to 50 mg. However, there would be little additional increase with further increase in the antibody dose (data not shown). Thus, once a “best-fit” model for the patient-specific tracer kinetics has been deduced, the optimum mAb dose can be determined by model simulation of tumor, blood, and antigen-expressing normal-tissue kinetics (including AUCs) for different, systematically varied mAb doses.

TABLE 1 “Best-Fit” Model Parameters and Model-derived Quantities for Intravenously Injected ¹²⁴I-anti-A33 Humanized mAb in Colorectal Cancer Patients (n = 11) Standard Mean Deviation Range Normal-bowel A33 concentration, 96 267 5-900 Total [A33]₂ (nM) Tumor A33 concentration, 140 225 2-650 Total [A33]₃ (nM) (Anti-A33 mAb)-A33 association rate 6.1 × 10¹⁰ 2.8 × 10¹⁰ 6.5 × 10⁹-1.1 × 10¹¹ constant, k_(a) (/M/h) Rate of elimination of ¹²⁴I-mAb from 0.012 0.0023 0.0093-0.018   plasma, k(0,1) (/h) Rate of elimination of ¹²⁴I-mAb from 0.0056^(a) 0.007  0-0.02 normal bowel, k(0,2) (/h) Rate of elimination of ¹²⁴I-mAb from 0.0033^(a) 0.0078   0-0.020 tumor, k(0,3) (/h) Tumor-to-plasma AUC concentration 6.1 8.8 0.16-26    (% ID/g-h) ratio^(b) Tumor-to-normal bowel mucosa AUC 4.5 5.1 0.13-16    concentration (% ID/g-h) ratio^(b, c) Maximum A33 occupancy (%)^(d) Tumor 53 41 1-100 Normal colon 27 17 0.30-43    ^(a)Only 1 of the 11 patients had an elimination rate from normal bowel of 0, while 8 of the 11 patients had an elimination rate from tumor of 0. ^(b)Corrected for radioactive decay of ¹²⁴I to the time of injection. ^(c)The mucosa (assumed mass in all patients: 7 g) is the A33-expressing component of the bowel wall. ^(d)Maximum value of the percent of total A33 antigenic sites with ¹²⁴I-anti-A33 humanized mAb bound.

In recent work demonstrating the utility of non-linear modeling in characterizing immune reagents in vivo, the binding affinities and internalization kinetics of ⁸⁹Zr-cG250 and ¹²⁴I-cG250, a chimeric antibody directed against carbonic anhydrase IX (CAIX), were characterized in a human renal-cell carcinoma xenograft model in mice (SK-RC-38) by serial PET imaging and non-linear modeling. This work is described in Cheal S M, Punzalan B, Doran M G, et al.: Pairwise comparison of ⁸⁹Zr- and ¹²⁴I-labeled cG250 based on positron emission tomography imaging and nonlinear immunokinetic modeling: in vivo carbonic anhydrase IX receptor binding and internalization in mouse xenografts of clear-cell renal cell carcinoma. Eur J Nucl Med Mol Imaging. 41:985-94, 2014, which is hereby incorporated by reference in its entirety. ⁸⁹Zr- and ¹²⁴I-cG250 demonstrated virtually identical tumor-cell binding and internalization rates but with markedly different tumor retentions. Estimates of cG250-CAIX complex turnover were 1.35-5.51×10¹² molecules per hour per gram of tumor (with 20% of the receptors internalized per hour), and the ratio of ¹²⁴I-to-⁸⁹Zr atoms released per unit time by tumor was 18.

The properties of A33, and other antigens, as an antigenic target can be exploited and the foregoing paradigm to individualized RIT, including MST-based RIT, of colorectal cancer and solid tumors generally, can be extended. As the data in Table 1 shows, directly labeled anti-A33 mAb typically yields tumor-to-normal tissue ratios of the order of only several-to-one or less, which may be inadequate for safe and effective RIT. The physiology of A33-expressing normal cells makes A33 an exemplary target for MST-based RIT of colon cancer. By temporally dissociating delivery of the A33 mAb construct (e.g., streptavidinated mAb or a bifunctional antibody construct) and the therapeutic radionuclide, potential therapy-limiting irradiation of the intestinal crypt cells is avoided because the A33 mAb-binding crypt cells are turned over and replaced prior to administration of the radionuclide carrier (e.g., metal chelator-biotin) designed to bind to the A33-mAb complex.

The MST strategy can utilize a bi-specific tetravalent anti-A33/-[metal-DOTA] antibody construct (nALDO) which reacts with A33 and with radiometal-labeled DOTA-Bn (DOTA-Bn: ((2-(4-aminobenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl) tetraacetic acid). Notably, these results demonstrate that acquisition and accurate non-linear modeling of kinetic data of A33 and tumor-targeted immune constructs are clinically practical. Further, the model utilized in the current analysis is adaptable to MST and thus can be used to predict not only the patient-specific optimum dose (in mole) of the construct but also the optimum times of administration of the clearing agent and of the radionuclide carrier. The current results thus demonstrate a practically achievable advancement in customized cancer therapy. Two clinically utilized long-lived positron-emitting isotopes ⁸⁹Zr and ¹²⁴I have differences in biologic fate following intracellular uptake, such as “residualizing” and “non-residualizing”, respectively. Pairwise evaluation of radiolabeled variants (such as “residualizing” and “non-residualizing”, respectively) of the same targeting antibody in pre-clinical animal models can be useful to estimate differential response of tracer metabolism and quantitate turnover of bound antibody, thus guiding personalized treatment in patients.

The present disclosure may be applicable to any antibody-antigen system for which antigen occupancy is the mechanism for anti-tumor effect or normal-organ toxicity. Moreover, the present disclosure provides the basis of a practical, scientifically rational, and cost-effective approach to the clinical optimization of such therapeutics.

Methods and Materials

A33 Antibody and Radiolabeling

HuA33 is a fully humanized IgG1 mAb derived from murine A33 antibody by complementarity determinant region (CDR) grafting and was produced and characterized at the Ludwig Institute for Cancer Research (Melbourne, Australia). HuA33 was labeled by the iodogen method with ¹²⁴I, produced in the Memorial Sloan-Kettering Cancer Center (MSKCC) cyclotron facility or purchased from IBA (Dulle, Va.). Formulations of between 1 mCi ¹²⁴I/10 mg A33 and 10 mCi ¹²⁴I/10 mg A33 were produced under current Good Manufacturing Practices (cGMP) conditions. Briefly, ¹²⁴I was incubated with huA33 (7 mg, 0.1 M phosphate buffer pH 7.4) in an iodogen tube. After a 20-minute incubation, the mixture was purified through an anion exchange column followed by terminal sterilization through a 0.22-μm filter. The activity of the final product was assayed in a dose calibrator and cold antibody was added to yield a specific activity of between 0.1 and 1 mCi/mg. All products passed pyrogen and sterility testing. The median radiochemical yield was 95%, radiochemical purity 99.2%, and immunoreactivity 89% as determined by a cell-binding assay using SW1222 cells. The use of a long-lived positron emitter, ¹²⁴I, and the ability to thereby extend kinetic measurements to ˜1 week post-injection was an important practical feature of the current study in terms of fitting of the disclosed model.

Patient Eligibility and Protocol Design

Patients were enrolled in an open-label prospective protocol approved by the MSKCC Institutional Review Board (IRB) to evaluate the safety, pharmacokinetics, biodistribution, and tumor-targeting properties of intravenously administered ¹²⁴I-huA33. Subjects were at least 18 years of age and had histologically confirmed primary or metastatic colorectal and were candidates for clinically indicated laparotomy for tumor resection or were non-surgical candidates with liver metastasis larger than 2.5 cm. All subjects provided written and oral informed consent. A subset of 11 (out of a total of 19) subjects having multiple PET or PET-CT scans were included in the current analysis.

Subjects received a single intravenous administration of ¹²⁴I-huA33 in 5-30 ml of 5% human serum albumin over 5 to 20 minutes. Patients scheduled for surgery underwent an ¹²⁴I-huA33 scan the morning of their surgery and surgically harvested tumor and normal colon were weighed and counted in a sodium iodide scintillation well counter with a ¹²⁴I standard to convert net count rates to activity and the activity concentration (percent of the injected dose per gram, % ID/g) decay-corrected to the time of injection was determined.

Imaging and Derivation of Tissue Activities

Patients were imaged on a General Electric Advance PET scanner or Discovery LS or DSTE PET-CT scanner in 2D mode with attenuation, scatter, and other standard corrections applied. Images were acquired for 6 minutes per bed position and included whole-torso imaging from the neck to the proximal thighs or from mid-chest to pelvis. For the cohort analyzed in the current study, patients had their initial images 3 hours of administration of ¹²⁴I-A33 and their final images at a median of 8.9 days after tracer administration.

Images were read on a dedicated PET analysis workstation (AW Suite, GE Healthcare, Barrington, Ill.) by an experienced nuclear medicine physician (JAC) who was aware of the patient's history and diagnostic CT report. Localization in tumor was defined as focal accumulation greater than adjacent background in areas where physiologic activity is negligible. Bowel activity foci were considered positive for tumor when there was an accumulation greater than that in adjacent bowel. Regions of interest (ROIs) were manually placed over structures of interest and mean standardized uptake values (SUVs) ((nCi/mL activity in region)/(nCi injected activity/body mass in g)) were determined for blood pool, tumor, and small and large bowel. In order to derive the mean tissue activity concentration in % ID/g, the SUV was divided by the body weight (in g) and multiplied by 100.

Tumor masses were estimated by CT (assuming a mass density of 1 g/ml) or by weighing of surgically excised lesions. By multiplying the tumor masses determined by the PET-derived activity concentrations (in % ID/g), the total activities (in percent of the injected dose, % ID) in tumor were determined at each of up to 4 imaging time points; an illustrative series of ¹²⁴I-huA33 PET images is shown in FIG. 1. A33-expressing cells in normal bowel are restricted to the intestinal mucosa, including the self-renewing intestinal crypt cells, and thus comprise only a small portion by mass of the bowel wall: of the 1,000-g total mass of the bowel wall in the 70-kg “Reference Man”, only 7 g comprise the intestinal mucosa according to the small-scale anatomic model for bowel. For all patients in the current analysis, therefore, the activities in the A33-expressing mucosa and the bowel wall were calculated using 7 and 1,000 g as the respective masses of these tissues.

Blood Sampling and Counting

Blood samples were drawn from all subjects from the contralateral antecubital vein as used for infusion at approximately 5, 15, 60, and 120 minutes post-antibody administration and on the last day of imaging or the day of surgery (5-9 days after antibody infusion); for some subjects, an additional blood sample was drawn at 24-72 h post-administration with concomitant imaging. All samples were centrifuged and plasma was aliquoted, weighed, and counted as described above for the tissue samples. The plasma activity concentrations in % ID/g were multiplied by the total plasma volume, calculated based on the patient's height and weight, to yield the total plasma activity in % ID.

Compartmental Modeling

The plasma, normal bowel, and tumor time-activity data for each patient were fit to the non-linear compartmental model, that is, the three differential equations, shown in FIG. 2 using the SAAM II program. The measured plasma time-activity data provided the direct input function for tumor and bowel, eliminating the need to explicitly account for the anti-A33 mAb in the rest of the body (e.g., in tissues other than plasma, bowel, and tumor). For fitting, the starting values of the association rate constant (k_(a)), the total A33 concentration in normal bowel ([A33]₂), and the total A33 concentration in tumor ([A33]₃) were 1×10¹¹/h/M, 2.5×10⁻⁸ M=24 nM, and 3.3×10⁻⁸ M=33 nM, respectively, as measured independently in vitro (data not shown). The other adjustable model parameters (with their starting values in parentheses) are: k(0,1) (0.01/h), k(0,2) (0.005/h), and k(0,3) (0/h); the value of k(0,2), 0.005/h, corresponds to a half-life of slightly less than 6 d, consistent with the turnover rate of the intestinal mucosa. The model in FIG. 2 is based off the following assumptions: (i) no free radioiodine or radioiodinated metabolites of the ¹²⁴I-labeled anti-A33 mAb are produced in vivo; (ii) there is no significant amount of free (e.g., non-A33-bound mAb) in normal bowel and in tumor; (iii) there is no significant dissociation of the bound anti-A33 mAb from A33 (as indicated by the absence of k(2,0) and k(3,0) exchange rates in the model and consistent with the finding of prolonged (up to 6-week) retention of radiolabeled A33 mAb in colorectal tumors); and (iv) the (anti-A33 mAb)-A33 association rate constant (k_(a)) is the same for A33 in normal bowel and in tumor. Note that k(2,1) equals k_(a)·{Total [A33]₂-[(¹²⁴I-mAb)-A33]₂} and that k(3,1) equals k_(a)·{Total [A33]₃-[(¹²⁴I-mAb)-A33]₃}; the second term (e.g., the differences) in these two formulas make k(2,1) and k(3,1) time-varying parameters and thus introduces non-linearities into the model. Evaluation of the values of the various “k” parameters in the model for specific patients is performed by computerized fitting of the model to the patient's measured time-activity data, that is, solving of the system of differential equations in FIG. 2. The foregoing assumptions do not limit the generality of the proposed methodology in that the nonlinear compartment model can be modified to account for those instances in which these assumptions may not be satisfied.

FIG. 4 shows an illustrative network environment 400 for use in the methods and systems for analysis of spectrometry data corresponding to particles of a sample, as described herein. In brief overview, referring now to FIG. 4, a block diagram of an exemplary cloud computing environment 400 is shown and described. The cloud computing environment 400 may include one or more resource providers 402 a, 402 b, 402 c (collectively, 402). Each resource provider 402 may include computing resources. In some implementations, computing resources may include any hardware and/or software used to process data. For example, computing resources may include hardware and/or software capable of executing algorithms, computer programs, and/or computer applications. In some implementations, exemplary computing resources may include application servers and/or databases with storage and retrieval capabilities. Each resource provider 402 may be connected to any other resource provider 402 in the cloud computing environment 400. In some implementations, the resource providers 402 may be connected over a computer network 408. Each resource provider 402 may be connected to one or more computing device 404 a, 404 b, 404 c (collectively, 404), over the computer network 408.

The cloud computing environment 400 may include a resource manager 406. The resource manager 406 may be connected to the resource providers 402 and the computing devices 404 over the computer network 408. In some implementations, the resource manager 406 may facilitate the provision of computing resources by one or more resource providers 402 to one or more computing devices 404. The resource manager 406 may receive a request for a computing resource from a particular computing device 404. The resource manager 406 may identify one or more resource providers 402 capable of providing the computing resource requested by the computing device 404. The resource manager 406 may select a resource provider 402 to provide the computing resource. The resource manager 406 may facilitate a connection between the resource provider 402 and a particular computing device 404. In some implementations, the resource manager 406 may establish a connection between a particular resource provider 402 and a particular computing device 404. In some implementations, the resource manager 406 may redirect a particular computing device 404 to a particular resource provider 402 with the requested computing resource.

FIG. 5 shows an example of a computing device 500 and a mobile computing device 550 that can be used in the methods and systems described in this disclosure. The computing device 500 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The mobile computing device 550 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting.

The computing device 500 includes a processor 502, a memory 504, a storage device 506, a high-speed interface 508 connecting to the memory 504 and multiple high-speed expansion ports 510, and a low-speed interface 512 connecting to a low-speed expansion port 514 and the storage device 506. Each of the processor 502, the memory 504, the storage device 506, the high-speed interface 508, the high-speed expansion ports 510, and the low-speed interface 512, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 502 can process instructions for execution within the computing device 500, including instructions stored in the memory 504 or on the storage device 506 to display graphical information for a GUI on an external input/output device, such as a display 516 coupled to the high-speed interface 508. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

The memory 504 stores information within the computing device 500. In some implementations, the memory 504 is a volatile memory unit or units. In some implementations, the memory 504 is a non-volatile memory unit or units. The memory 504 may also be another form of computer-readable medium, such as a magnetic or optical disk.

The storage device 506 is capable of providing mass storage for the computing device 500. In some implementations, the storage device 506 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. Instructions can be stored in an information carrier. The instructions, when executed by one or more processing devices (for example, processor 502), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices such as computer- or machine-readable mediums (for example, the memory 504, the storage device 506, or memory on the processor 502).

The high-speed interface 508 manages bandwidth-intensive operations for the computing device 500, while the low-speed interface 512 manages lower bandwidth-intensive operations. Such allocation of functions is an example only. In some implementations, the high-speed interface 508 is coupled to the memory 504, the display 516 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 510, which may accept various expansion cards (not shown). In the implementation, the low-speed interface 512 is coupled to the storage device 506 and the low-speed expansion port 514. The low-speed expansion port 514, which may include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.

The computing device 500 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 520, or multiple times in a group of such servers. In addition, it may be implemented in a personal computer such as a laptop computer 522. It may also be implemented as part of a rack server system 524. Alternatively, components from the computing device 500 may be combined with other components in a mobile device (not shown), such as a mobile computing device 550. Each of such devices may contain one or more of the computing device 500 and the mobile computing device 550, and an entire system may be made up of multiple computing devices communicating with each other.

The mobile computing device 550 includes a processor 552, a memory 564, an input/output device such as a display 554, a communication interface 566, and a transceiver 568, among other components. The mobile computing device 550 may also be provided with a storage device, such as a micro-drive or other device, to provide additional storage. Each of the processor 552, the memory 564, the display 554, the communication interface 566, and the transceiver 568, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.

The processor 552 can execute instructions within the mobile computing device 550, including instructions stored in the memory 564. The processor 552 may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor 552 may provide, for example, for coordination of the other components of the mobile computing device 550, such as control of user interfaces, applications run by the mobile computing device 550, and wireless communication by the mobile computing device 550.

The processor 552 may communicate with a user through a control interface 558 and a display interface 556 coupled to the display 554. The display 554 may be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 556 may comprise appropriate circuitry for driving the display 554 to present graphical and other information to a user. The control interface 558 may receive commands from a user and convert them for submission to the processor 552. In addition, an external interface 562 may provide communication with the processor 552, so as to enable near area communication of the mobile computing device 550 with other devices. The external interface 562 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

The memory 564 stores information within the mobile computing device 550. The memory 564 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory 574 may also be provided and connected to the mobile computing device 550 through an expansion interface 572, which may include, for example, a SIMM (Single In Line Memory Module) card interface. The expansion memory 574 may provide extra storage space for the mobile computing device 550, or may also store applications or other information for the mobile computing device 550. Specifically, the expansion memory 574 may include instructions to carry out or supplement the processes described above, and may include secure information also. Thus, for example, the expansion memory 574 may be provided as a security module for the mobile computing device 550, and may be programmed with instructions that permit secure use of the mobile computing device 550. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory (non-volatile random access memory), as discussed below. In some implementations, instructions are stored in an information carrier and, when executed by one or more processing devices (for example, processor 552), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 564, the expansion memory 574, or memory on the processor 552). In some implementations, the instructions can be received in a propagated signal, for example, over the transceiver 568 or the external interface 562.

The mobile computing device 550 may communicate wirelessly through the communication interface 566, which may include digital signal processing circuitry where necessary. The communication interface 566 may provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others. Such communication may occur, for example, through the transceiver 568 using a radio-frequency. In addition, short-range communication may occur, such as using a Bluetooth®, Wi-Fi™, or other such transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module 570 may provide additional navigation- and location-related wireless data to the mobile computing device 550, which may be used as appropriate by applications running on the mobile computing device 550.

The mobile computing device 550 may also communicate audibly using an audio codec 560, which may receive spoken information from a user and convert it to usable digital information. The audio codec 560 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 550. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on the mobile computing device 550.

The mobile computing device 550 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 580. It may also be implemented as part of a smart-phone 582, personal digital assistant, or other similar mobile device.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

Example 2: Comparative Biodistribution of Zirconium-89 (⁸⁹Zr)-DFO-hu11B6 and Actinium-225 (²²⁵Ac)-DOTA-hu11B6 Antibody Targeting Breast Cancer in Mice Bearing Human Breast Tumor Xenografts

Zirconium-89 (⁸⁹Zr) is a relatively-lived positron-emitting radioisotope (physical half-life: 3.27 days) which has been successfully used to radiolabel and image positron emission tomography (PET) human tumor xenografts in mice using tumor-targeted antibodies, several of which are now in clinical trial, for example, ⁸⁹Zr-trastuzumab, ⁸⁹Zr-huJ591, ⁸⁹Zr-MSTP2109A, ⁸⁹Zr-Df-IAB2M, ⁸⁹Zr-cmAb-U36, ⁸⁹Zr-ibritumomab tiuxetan, ⁸⁹Zr-bevacizumab, and ⁸⁹Zr-rituximab. By serial PET imaging, the tumor and normal-tissue kinetics (e.g., time-activity data) of such ⁸⁹Zr-labeled antibodies can be non-invasively measured in individual patients. The non-linear compartmental model of antibody kinetics described in herein is completely general and can be fit to the ⁸⁹Zr-labeled antibody kinetics measured in specific patients.

As described herein, the fitted patient-specific model can be used for computer-based simulations of the antibody kinetics as a function of the antibody dose (in milligrams or millimoles, for example) and an optimum antibody dose determined. The optimum dose comprises the dose that yields the highest tumor-to-normal tissue ratio of the antibody concentration integrated over time. The patient can then be administered his or her model-defined optimum antibody dose with the antibody now carrying a therapeutic payload such as anti-cancer drug or a therapeutic radionuclide.

In certain embodiments, anti-cancer drugs include any of the more than 200 cancer drugs identified by the NIH at http://www.cancer.gov/about-cancer/treatment/drugs, the contents of which are hereby incorporated by reference in its entirety. Table 2 shows exemplary drugs identified by the NIH.

TABLE 2 Exemplary drugs (Names A-J) Exemplary drugs (Names K-Z) Abiraterone Acetate Kadcyla (Ado-Trastuzumab Emtansine) Abitrexate (Methotrexate) Keoxifene (Raloxifene Hydrochloride) Abraxane (Paclitaxel Albumin-stabilized Kepivance (Palifermin) Nanoparticle Formulation) ABVD Keytruda (Pembrolizumab) ABVE Kyprolis (Carfilzomib) ABVE-PC Lanreotide Acetate AC Lapatinib Ditosylate AC-T Lenalidomide Adcetris (Brentuximab Vedotin) Lenvatinib Mesylate ADE Lenvima (Lenvatinib Mesylate) Ado-Trastuzumab Emtansine Letrozole Adriamycin (Doxorubicin Hydrochloride) Leucovorin Calcium Afatinib Dimaleate Leukeran (Chlorambucil) Afinitor (Everolimus) Leuprolide Acetate Akynzeo (Netupitant and Palonosetron Levulan (Aminolevulinic Acid) Hydrochloride) Aldara (Imiquimod) Linfolizin (Chlorambucil) Aldesleukin LipoDox (Doxorubicin Hydrochloride Liposome) Alecensa (Alectinib) Lomustine Alectinib Lonsurf (Trifluridine and Tipiracil Hydrochloride) Alemtuzumab Lupron (Leuprolide Acetate) Alkeran for Injection (Melphalan Lupron Depot (Leuprolide Acetate) Hydrochloride) Alkeran Tablets (Melphalan) Lupron Depot-Ped (Leuprolide Acetate) Alimta (Pemetrexed Disodium) Lupron Depot-3 Month (Leuprolide Acetate) Aloxi (Palonosetron Hydrochloride) Lupron Depot-4 Month (Leuprolide Acetate) Ambochlorin (Chlorambucil) Lynparza (Olaparib) Amboclorin (Chlorambucil) Marqibo (Vincristine Sulfate Liposome) Aminolevulinic Acid Matulane (Procarbazine Hydrochloride) Anastrozole Mechlorethamine Hydrochloride Aprepitant Megestrol Acetate Aredia (Pamidronate Disodium) Mekinist (Trametinib) Arimidex (Anastrozole) Melphalan Aromasin (Exemestane) Melphalan Hydrochloride Arranon (Nelarabine) Mercaptopurine Arsenic Trioxide Mesna Arzerra (Ofatumumab) Mesnex (Mesna) Asparaginase Erwinia chrysanthemi Methazolastone (Temozolomide) Avastin (Bevacizumab) Methotrexate Axitinib Methotrexate LPF (Methotrexate) Azacitidine Mexate (Methotrexate) BEACOPP Mexate-AQ (Methotrexate) Becenum (Carmustine) Mitomycin C Beleodaq (Belinostat) Mitoxantrone Hydrochloride Belinostat Mitozytrex (Mitomycin C) Bendamustine Hydrochloride MOPP BEP Mozobil (Plerixafor) Bevacizumab Mustargen (Mechlorethamine Hydrochloride) Bexarotene Mutamycin (Mitomycin C) Bexxar (Tositumomab and Iodine I 131 Myleran (Busulfan) Tositumomab) Bicalutamide Mylosar (Azacitidine) BiCNU (Carmustine) Mylotarg (Gemtuzumab Ozogamicin) Bleomycin Nanoparticle Paclitaxel (Paclitaxel Albumin- stabilized Nanoparticle Formulation) Blinatumomab Navelbine (Vinorelbine Tartrate) Blincyto (Blinatumomab) Necitumumab Bortezomib Nelarabine Bosulif (Bosutinib) Neosar (Cyclophosphamide) Bosutinib Netupitant and Palonosetron Hydrochloride Brentuximab Vedotin Neupogen (Filgrastim) Busulfan Nexavar (Sorafenib Tosylate) Busulfex (Busulfan) Nilotinib Cabazitaxel Ninlaro (Ixazomib Citrate) Cabometyx (Cabozantinib-S-Malate) Nivolumab Cabozantinib-S-Malate Nolvadex (Tamoxifen Citrate) CAF Nplate (Romiplostim) Campath (Alemtuzumab) Obinutuzumab Camptosar (Irinotecan Hydrochloride) Odomzo (Sonidegib) Capecitabine OEPA CAPOX Ofatumumab Carac (Fluorouracil--Topical) OFF Carboplatin Olaparib CARBOPLATIN-TAXOL Omacetaxine Mepesuccinate Carfilzomib Oncaspar (Pegaspargase) Carmubris (Carmustine) Ondansetron Hydrochloride Carmustine Onivyde (Irinotecan Hydrochloride Liposome) Carmustine Implant Ontak (Denileukin Diftitox) Casodex (Bicalutamide) Opdivo (Nivolumab) CeeNU (Lomustine) OPPA CEM Osimertinib Ceritinib Oxaliplatin Cerubidine (Daunorubicin Paclitaxel Hydrochloride) Cervarix (Recombinant HPV Bivalent Paclitaxel Albumin-stabilized Nanoparticle Vaccine) Formulation Cetuximab PAD Chlorambucil Palbociclib CHLORAMBUCIL-PREDNISONE Palifermin CHOP Palonosetron Hydrochloride Cisplatin Palonosetron Hydrochloride and Netupitant Clafen (Cyclophosphamide) Pamidronate Disodium Clofarabine Panitumumab Clofarex (Clofarabine) Panobinostat Clolar (Clofarabine) Paraplat (Carboplatin) CMF Paraplatin (Carboplatin) Cobimetinib Pazopanib Hydrochloride Cometriq (Cabozantinib-S-Malate) PCV COPDAC Pegaspargase COPP Peginterferon Alfa-2b COPP-ABV PEG-Intron (Peginterferon Alfa-2b) Cosmegen (Dactinomycin) Pembrolizumab Cotellic (Cobimetinib) Pemetrexed Disodium Crizotinib Perjeta (Pertuzumab) CVP Pertuzumab Cyclophosphamide Platinol (Cisplatin) Cyfos (Ifosfamide) Platinol-AQ (Cisplatin) Cyramza (Ramucirumab) Plerixafor Cytarabine Pomalidomide Cytarabine Liposome Pomalyst (Pomalidomide) Cytosar-U (Cytarabine) Ponatinib Hydrochloride Cytoxan (Cyclophosphamide) Portrazza (Necitumumab) Dabrafenib Pralatrexate Dacarbazine Prednisone Dacogen (Decitabine) Procarbazine Hydrochloride Dactinomycin Proleukin (Aldesleukin) Daratumumab Prolia (Denosumab) Darzalex (Daratumumab) Promacta (Eltrombopag Olamine) Dasatinib Provenge (Sipuleucel-T) Daunorubicin Hydrochloride Purinethol (Mercaptopurine) Decitabine Purixan (Mercaptopurine) Defibrotide Sodium Radium 223 Dichloride Defitelio (Defibrotide Sodium) Raloxifene Hydrochloride Degarelix Ramucirumab Denileukin Diftitox Rasburicase Denosumab R-CHOP DepoCyt (Cytarabine Liposome) R-CVP Dexamethasone Recombinant Human Papillomavirus (HPV) Bivalent Vaccine Dexrazoxane Hydrochloride Recombinant Human Papillomavirus (HPV) Nonavalent Vaccine Dinutuximab Recombinant Human Papillomavirus (HPV) Quadrivalent Vaccine Docetaxel Recombinant Interferon Alfa-2b Doxil (Doxorubicin Hydrochloride Regorafenib Liposome) Doxorubicin Hydrochloride R-EPOCH Doxorubicin Hydrochloride Liposome Revlimid (Lenalidomide) Dox-SL (Doxorubicin Hydrochloride Rheumatrex (Methotrexate) Liposome) DTIC-Dome (Dacarbazine) Rituxan (Rituximab) Efudex (Fluorouracil--Topical) Rituximab Elitek (Rasburicase) Rolapitant Hydrochloride Ellence (Epirubicin Hydrochloride) Romidepsin Elotuzumab Romiplostim Eloxatin (Oxaliplatin) Rubidomycin (Daunorubicin Hydrochloride) Eltrombopag Olamine Ruxolitinib Phosphate Emend (Aprepitant) Sclerosol Intrapleural Aerosol (Talc) Empliciti (Elotuzumab) Siltuximab Enzalutamide Sipuleucel-T Epirubicin Hydrochloride Somatuline Depot (Lanreotide Acetate) EPOCH Sonidegib Erbitux (Cetuximab) Sorafenib Tosylate Eribulin Mesylate Sprycel (Dasatinib) Erivedge (Vismodegib) STANFORD V Erlotinib Hydrochloride Sterile Talc Powder (Talc) Erwinaze (Asparaginase Erwinia Steritalc (Talc) chrysanthemi) Etopophos (Etoposide Phosphate) Stivarga (Regorafenib) Etoposide Sunitinib Malate Etoposide Phosphate Sutent (Sunitinib Malate) Evacet (Doxorubicin Hydrochloride Sylatron (Peginterferon Alfa-2b) Liposome) Everolimus Sylvant (Siltuximab) Evista (Raloxifene Hydrochloride) Synovir (Thalidomide) Evomela (Melphalan Hydrochloride) Synribo (Omacetaxine Mepesuccinate) Exemestane Tabloid (Thioguanine) 5-FU (Fluorouracil Injection) TAC 5-FU (Fluorouracil--Topical) Tafinlar (Dabrafenib) Fareston (Toremifene) Tagrisso (Osimertinib) Farydak (Panobinostat) Talc Faslodex (Fulvestrant) Talimogene Laherparepvec FEC Tamoxifen Citrate Femara (Letrozole) Tarabine PFS (Cytarabine) Filgrastim Tarceva (Erlotinib Hydrochloride) Fludara (Fludarabine Phosphate) Targretin (Bexarotene) Fludarabine Phosphate Tasigna (Nilotinib) Fluoroplex (Fluorouracil--Topical) Taxol (Paclitaxel) Fluorouracil Injection Taxotere (Docetaxel) Fluorouracil--Topical Temodar (Temozolomide) Flutamide Temozolomide Folex (Methotrexate) Temsirolimus Folex PFS (Methotrexate) Thalidomide FOLFIRI Thalomid (Thalidomide) FOLFIRI-BEVACIZUMAB Thioguanine FOLFIRI-CETUXIMAB Thiotepa FOLFIRINOX Tolak (Fluorouracil--Topical) FOLFOX Topotecan Hydrochloride Folotyn (Pralatrexate) Toremifene FU-LV Torisel (Temsirolimus) Fulvestrant Tositumomab and Iodine I 131 Tositumomab Gardasil (Recombinant HPV Totect (Dexrazoxane Hydrochloride) Quadrivalent Vaccine) Gardasil 9 (Recombinant HPV TPF Nonavalent Vaccine) Gazyva (Obinutuzumab) Trabectedin Gefitinib Trametinib Gemcitabine Hydrochloride Trastuzumab GEMCITABINE-CISPLATIN Treanda (Bendamustine Hydrochloride) GEMCITABINE-OXALIPLATIN Trifluridine and Tipiracil Hydrochloride Gemtuzumab Ozogamicin Trisenox (Arsenic Trioxide) Gemzar (Gemcitabine Hydrochloride) Tykerb (Lapatinib Ditosylate) Gilotrif (Afatinib Dimaleate) Unituxin (Dinutuximab) Gleevec (Imatinib Mesylate) Uridine Triacetate Gliadel (Carmustine Implant) VAC Gliadel wafer (Carmustine Implant) Vandetanib Glucarpidase VAMP Goserelin Acetate Varubi (Rolapitant Hydrochloride) Halaven (Eribulin Mesylate) Vectibix (Panitumumab) Herceptin (Trastuzumab) VeIP HPV Bivalent Vaccine, Recombinant Velban (Vinblastine Sulfate) HPV Nonavalent Vaccine, Recombinant Velcade (Bortezomib) HPV Quadrivalent Vaccine, Recombinant Velsar (Vinblastine Sulfate) Hycamtin (Topotecan Hydrochloride) Vemurafenib Hydrea (Hydroxyurea) Venclexta (Venetoclax) Hydroxyurea Venetoclax Hyper-CVAD Viadur (Leuprolide Acetate) Ibrance (Palbociclib) Vidaza (Azacitidine) Ibritumomab Tiuxetan Vinblastine Sulfate Ibrutinib Vincasar PFS (Vincristine Sulfate) ICE Vincristine Sulfate Iclusig (Ponatinib Hydrochloride) Vincristine Sulfate Liposome Idamycin (Idarubicin Hydrochloride) Vinorelbine Tartrate Idarubicin Hydrochloride VIP Idelalisib Vismodegib Ifex (Ifosfamide) Vistogard (Uridine Triacetate) Ifosfamide Voraxaze (Glucarpidase) Ifosfamidum (Ifosfamide) Vorinostat IL-2 (Aldesleukin) Votrient (Pazopanib Hydrochloride) Imatinib Mesylate Wellcovorin (Leucovorin Calcium) Imbruvica (Ibrutinib) Xalkori (Crizotinib) Imiquimod Xeloda (Capecitabine) Imlygic (Talimogene Laherparepvec) XELIRI Inlyta (Axitinib) XELOX Interferon Alfa-2b, Recombinant Xgeva (Denosumab) Interleukin-2 (Aldesleukin) Xofigo (Radium 223 Dichloride) Intron A (Recombinant Interferon Alfa- Xtandi (Enzalutamide) 2b) Iodine I 131 Tositumomab and Yervoy (Ipilimumab) Tositumomab Ipilimumab Yondelis (Trabectedin) Iressa (Gefitinib) Zaltrap (Ziv-Aflibercept) Irinotecan Hydrochloride Zarxio (Filgrastim) Irinotecan Hydrochloride Liposome Zelboraf (Vemurafenib) Istodax (Romidepsin) Zevalin (Ibritumomab Tiuxetan) Ixabepilone Zinecard (Dexrazoxane Hydrochloride) Ixazomib Citrate Ziv-Aflibercept Ixempra (Ixabepilone) Zofran (Ondansetron Hydrochloride) Jakafi (Ruxolitinib Phosphate) Zoladex (Goserelin Acetate) Jevtana (Cabazitaxel) Zoledronic Acid Zolinza (Vorinostat) Zometa (Zoledronic Acid) Zydelig (Idelalisib) Zykadia (Ceritinib) Zytiga (Abiraterone Acetate)

In certain embodiments, anti-cancer drugs used herein are classes of drugs including, but not limited to the following: calicheamicin, doxirubicin, dolstatin/auristatin, maytansine, emtansine, ravtansine, alpha amanitin, pyrolobenzodiazapine, tubulysins, dasatinib and other pathway inhibitors, and bevatuzimab.

In certain embodiments, anti-cancer drugs used herein are classes of drugs targeting human epidermal growth factor receptor 3 (HER3), such as GSK2849330. HER3 expression is seen across a wide variety of solid malignancies and is associated with poor prognosis. Up-regulation of HER3 expression and activity is also associated with resistance to multiple pathway inhibitors. In certain embodiments, an anti-cancer drug used herein includes GSK2849330, a mAb targeting HER3. In certain embodiments, the disclosed non-linear model characterizes the biodistribution and dose-receptor occupancy relationship of GSK2849330 in subjects with advanced HER3 expressing solid tumors via PET imaging. Such a characterization is conducted in two parts. Part 1 includes the imaging phase where each subject will receive two doses of GSK2849330 containing both ⁸⁹Zr labelled GSK2849330 and unlabeled GSK2849330. The amount of unlabeled GSK2849330 present in each dose is varied to explore the effect on target mediated uptake of ⁸⁹Zr into HER3 expressing tissues and tumors. Subjects then proceed to the continuation phase (or Part 2) for continued treatment with unlabeled GSK2849330.

In certain embodiments, anti-cancer drugs used herein are classes of drugs targeting human carcinoembryonic antigen (e.g., CEA, CD66e) on cells (e.g., tumor cells, cluster of differentiation 3 (CD3) positive T-cells), such as AMG 211. AMG 211 is a bispecific single-chain antibody construct of the bispecific T-cell engager class and is a targeted drug in the treatment of relapsed/refractory gastrointestinal adenocarcinoma, or other CEA expressing tumors.

A well-known challenge in current drug development using targeted therapies is the high level of heterogeneity of target expression that is present in specific tumor types. The disclosed non-linear compartmental model, including radiolabeling of antibodies, provides a methodology to overcome this challenge.

In FIG. 6, the biodistributions (or the percent of the administered activity per gram of tissue, % IA/g) at 244 hours post-administration of ⁸⁹Zr-DFO-hu11B6 and ²²⁵Ac-DOTA-hu11B6 antibody targeting breast cancer in mice bearing human BT474 breast tumor xenografts are compared and shown, within experimental error, to be identical (lg-int: large intestine; sm-int: small intestine). Notably, even with the administration of hormones, progesterone (“Prog.”) or estrogen (“Estro.”), which impact the levels of expression of the hu11B6 target antigen, the biodistributions of these two radiolabeled forms of the hu11B6 antibody (Diaprost, Inc.; Lund, Sweden) remained identical. Thus, (1) the biodistribution, including tumor uptake, of the therapeutic ²²⁵Ac-DOTA-hu11B6 antibody can be accurately measured prior to therapy with the diagnostic ⁸⁹Zr-DFO-hu11B6 antibody and (2) the model-predicted optimum dose of hu11B6 antibody, based on the measured kinetics of ⁸⁹Zr-DFO-hu11B6 antibody, is the same for both ⁸⁹Zr-DFO-hu11B6 and ²²⁵Ac-DOTA-hu11B6 antibody. While the biodistribution data shown in FIG. 6 are for single time point, 244 hours, post administration of radiolabeled hu11B6, as with all systemically administered materials, can change the biodistribution profiles with time and thus require kinetic (e.g., time-varying) analysis (such as that provided by the non-linear compartmental modeling paradigm) to reliably derive an optimum antibody dose for a given patient.

²²⁵Ac is an alpha particle-emitting radioisotope. Although alpha particles-emitters are not yet in widespread clinical use, they have unique and highly advantageous physical and biological properties for targeted therapy: (1) their ranges in tissues are very short (of the order of one hundredth of a millimeter or approximately one cell radius) and thus will deliver their radiation doses very locally and very selectively to the targeted tumor cells with little or no significant irradiation of nearby normal cells; and (2) their very high so-called linear energy transfer (or LET) and resulting ionization density means that as few a single alpha-particle traversing a tumor-cell nucleus can kill the cell. Alpha-particles emitting radionuclides such as ²²⁵Ac are therefore highly attractive therapeutic payloads for application to the strategy described herein. And, to reiterate, the identical biodistributions of ⁸⁹Zr-DFO-hu11B6 and ²²⁵Ac-DOTA-hu11B6 antibody strongly support the clinical feasibility of this novel strategy.

Example 3: Targeting of Radiolabeled J591 Antibody to PSMA-expressing tumors: Optimization of Imaging and Therapy Based on Non-Linear Compartmental Modeling

Further confirming the workability of the non-linear compartmental model, FIG. 7 shows the disclosed non-linear compartmental model applied to a quantitative comparison of absolute uptake and antibody turnover in prostate cancer (PCa) of the anti-prostate-specific membrane antigen (PSMA) antibody J591 labeled with ⁸⁹Zr (or ⁸⁹Zr-J591). The model was applied to serial PET data measured following intravenous injection into mice each bearing a subcutaneous human PCa xenograft tumor. In addition to modeling “low” radiolabeled antibody doses (e.g., less than 100 micrograms of Ab per mouse), tumor-bearing mice were also administered increasing doses of radiolabeled J591 antibody up to 2,000 micrograms per mouse, and the time-activity data in the respective groups of mice again measured by PET. Among the parameters derived from the resulting kinetic data was the so-called “targeting index” (e.g., the tumor-to-plasma ratio of the integrals of the tumor and plasma activity concentrations); the targeting index is a metric of the overall therapeutic effectiveness of radiolabeled J591 therapy because it reflects the relative radiation doses delivered to the target tissue (e.g., the tumor) and the therapy-limiting normal tissue (e.g., the plasma, or blood, as a surrogate of the hematopoietic bone marrow). As shown in FIG. 7, the non-linear antibody compartmental model, in contrast to the corresponding linear model, accurately predicted the targeting index as function of antibody dose, confirming that only non-linear modeling (as described herein) can reliably be used for patients-specific dose optimization. Note that the data in FIG. 7 have been published as follows: Fung E K, Cheal S M, Fareedy S B, Punzalan B, Beylergil V, Amir J, Chalasani S, Weber W A, Spratt D E, Veach D R, Bander N H, Larson S M, Zanzonico P B, Osborne J R. Targeting of radiolabeled J591 antibody to PSMA-expressing tumors: optimization of imaging and therapy based on non-linear compartmental modeling. Eur J Nucl Med Mol Imaging Res 6, 2016, which is incorporated by reference herein in its entirety.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned herein are hereby incorporated by reference in their entirety as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference. In case of conflict, the present application, including any definitions herein, will control.

EQUIVALENTS

While the invention has been particularly shown and described with reference to specific preferred embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. 

What is claimed is:
 1. A method for determining a patient-specific optimum therapeutic mAb dose, the method comprising: administering to a subject a radiolabeled monoclonal antibody (mAb) as pre-therapy tracer of the mAb; imaging the subject to obtain a plurality of sets of images acquired over a period of time, wherein each set of images is acquired at a particular time following administration of the radiolabeled monoclonal antibody, and wherein each set of images visualizes a blood pool, tumor, and antigen-positive normal tissue; determining, by a processor of a computing device, from the plurality of sets of images, measurements of time-activity of the pre-therapy tracer of the mAb in blood, tumor, and antigen-expressing normal tissue for the subject; identifying, by the processor, one or more parameters of a nonlinear compartmental model of a time-varying biodistribution of the pre-therapy tracer in the subject from the determined measurements of time-activity for the subject, wherein the one or more parameters comprises a targeting index that reflects a relative amount of the pre-therapy tracer delivered to the tumor and wherein the targeting index comprises a tumor-to-plasma ratio of integrals of tumor and plasma activity concentrations, wherein the nonlinear compartmental model comprises a system of differential equations expressing rate of change in the amount of pre-therapy tracer in each of three compartments comprising a plasma compartment, a normal tissue compartment, and a tumor tissue compartment, wherein model nonlinearity is introduced by: expressing flux of the pre-therapy tracer from plasma to normal tissue using a first time-varying parameter proportional to a first concentration, wherein the first concentration is the difference between a total antigen concentration in the normal tissue compartment and unbound pre-therapy tracer concentration in the normal tissue compartment which varies with time and expressing flux of the pre-therapy tracer from plasma to tumor tissue using a time-varying parameter proportional to a second concentration, wherein the second concentration is the difference between a total antigen concentration in the tumor tissue compartment and unbound pre-therapy tracer concentration in the tumor tissue compartment which varies with time, and wherein evaluation of the one or more parameters for the subject is performed by fitting the nonlinear compartmental model to the subject's measured time-activity data; and determining, by the processor, the patient-specific optimal therapeutic antibody dose for the subject using the nonlinear compartmental model with the identified one or more parameters, wherein the patient-specific optimal therapeutic antibody dose is that which yields the maximum tumor-to-normal-tissue cumulated activity concentration ratio.
 2. The method of claim 1, wherein the monoclonal antibody is selected from the group consisting of A33 antibody, hu11B6 antibody, J591 antibody, trastuzumab, MSTP2109A, Df-IAB2M, cmAb-U36, ibritumomab tiuxetan, bevacizumab, rituximab, human epidermal growth factor receptor 3antibody, and human carcinoembryonic antigen antibody.
 3. The method of claim 1, wherein the radiolabel is selected from the group consisting of ^(99m)Tc, ¹¹¹In, ⁶⁴Cu, ⁶⁷Ga, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁷⁷Lu, ⁶⁷Cu, ¹²³I, ¹²⁴I, ¹²⁵I, ¹¹C, ¹³N, ¹⁵O, ¹⁸F, ¹⁶⁶Ho, ¹⁴⁹Pm, ⁹⁰Y, ²¹³Bi, ¹⁰³Pd, ¹⁰⁹Pd, ¹⁵⁹Gd, ¹⁴⁰La, ¹⁹⁸Au, ¹⁹⁹Au, ¹⁶⁹Yb, ¹⁷⁵Yb, ¹⁶⁵Dy, ¹⁶⁶Dy, ¹⁰⁵Rh, ¹¹¹Ag, ⁸⁹Zr, ²²⁵Ac, ¹³¹I, ²¹²B, and ¹⁹²Ir.
 4. The method of claim 3, wherein the radiolabel comprises ⁸⁹Zr.
 5. The method of claim 3, wherein the radiolabel comprises ²²⁵Ac.
 6. The method of claim 3, wherein the radiolabel comprises ¹²⁴I.
 7. The method of claim 1, further comprising acquiring the plurality of sets of images via positron emission tomography (PET).
 8. The method of claim 7, wherein a first set of the images is acquired several hours after antibody administration and at least a second set of the images is acquired up to 2 weeks after antibody administration.
 9. The method of claim 8, wherein the first set of images is acquired at least 2 hours, at least 3 hours, at least 4 hours, or at least 5 hours after antibody administration.
 10. The method of claim 1, wherein the antigen-positive normal tissue comprises colon tissue.
 11. The method of claim 1, wherein the antigen-positive normal tissue comprises breast tissue.
 12. The method of claim 1, wherein the antigen-positive normal tissues comprise prostate tissue.
 13. The method of claim 1, wherein the therapeutic antibody is the same monoclonal antibody as in the pre-therapy tracer.
 14. The method of claim 1, wherein the therapeutic antibody is different than the monoclonal antibody in the pre-therapy tracer.
 15. The method of claim 1, wherein the measurements of time-activity of the pre-therapy tracer comprise one or more members selected from the group consisting of a mean activity concentration of the radiolabeled antibody in the blood, a mean activity concentration of the radiolabeled antibody in the tumor, and a mean activity concentration of the radiolabeled antibody in the antigen-expressing normal tissue.
 16. The method of claim 15, wherein the antigen-expressing normal tissue comprises a member selected from the group consisting of bowel tissue, breast tissue, prostate tissue, kidney, liver, salivary gland, and lung.
 17. The method of claim 1, further comprising imaging the subject via computed tomography (CT) or magnetic resonance imaging (MRI) to determine a tumor mass.
 18. The method of claim 17, wherein a mean activity concentration of the radiolabeled antibody in the tumor is determined, by the processor, from a total activity concentration by multiplying the tumor mass by the total activity concentration and an elapsed time after the administering step.
 19. The method of claim 1, wherein the radiolabeled mAb comprises a radiolabel with a half-life compatible with imaging-based activity concentration measurements in tumor and other tissues.
 20. The method of claim 19, wherein the half-life is consistent with the turnover rate of intestinal mucosa in the case of A33 mAb.
 21. The method of claim 1, wherein the radiolabeled mAb is administered over a period of time from 5 minutes to 20 minutes.
 22. The method of claim 1, wherein the administering step comprises administering two or more doses of the pre-therapy tracer at different times, and wherein the method further comprises determining, by the processor, one or more optimum times of administration of the pre-therapy tracer.
 23. The method of claim 22, wherein the pre-therapy tracer comprises a bi-specific tetravalent antibody-[metal-DOTA] antibody construct which reacts with an antigen and with radiometal-labeled DOTA-Bn (DOTA-Bn: (2-(4-aminobenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl) tetraacetic acid) and/or DOTA (DOTA: (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl) tetraacetic acid).
 24. The method of claim 23, wherein the antibody is a member selected from the group consisting of A33 antibody, hu11B6 antibody, and J591 antibody and the antigen is a member selected from the group consisting of A33, hu11B6, and J591.
 25. The method of claim 1, wherein the nonlinear compartmental model comprises one or more of the following assumptions: (i) no free radiolabeled metabolites of the radiolabeled mAb are produced in vivo; (ii) there is no significant amount of free (non-bound) radiolabeled mAb in normal tissue and in tumor tissue; (iii) there is no significant dissociation of the bound radiolabeled mAb from the antigen; and (iv) the mAb-antigen association rate constant (ka) is the same for antigen in normal tissue and in tumor.
 26. The method of claim 25, wherein the radiolabeled mAb is a member selected from the group consisting of ¹²⁴I-labeled anti-A33, ⁸⁹Zr-labeled anti-hu11B6, ⁸⁹Zr-labeled anti-J591, ⁸⁹Zr-trastuzumab, ⁸⁹Zr-MSTP2109A, ⁸⁹Zr-Df-IAB2M, ⁸⁹Zr-cmAb-U36, ⁸⁹Zr-ibritumomab tiuxetan, ⁸⁹Zr-bevacizumab, and ⁸⁹Zr-rituximab.
 27. The method of claim 25, wherein the antigen is a member selected from the group consisting of A33, hu11B6, J591, GPA33, kallikrein hk2, and prostate specific membrane (PSMA).
 28. The method of claim 25, wherein the tissue is a member selected from the group consisting of bowel tissue, breast tissue, prostate tissue, kidney, liver, salivary gland, and lung.
 29. The method of claim 1, comprising determining, by the processor, a quantification of internalization and turnover of an antibody-cargo.
 30. The method of claim 29, wherein the antibody-cargo comprises a member selected from the group consisting of a radiolabel, anti-cancer drug, fluorescent probes, porphyrins, radiographic contrast agents, and cytotoxin.
 31. The method of claim 30, wherein the anti-cancer drug comprises a member selected from the group consisting of calicheamicin, doxirubicin, auristatin, maytansine, emtansine, ravtansine, alpha amanitin, pyrolobenzodiazapine, tubulysins, rituximab, trastuzumab, bevacizumab, anti-huJ591, MSTP2109A, Df-IAB2M, cmAb-U36, ibritumomab tiuxetan, bevacizumab, rituximab, human epidermal growth factor receptor 3antibody, and human carcinoembryonic antigen antibody, dasatinib bevatuzimab, cetuximab, and at least one drug depicted in Table
 2. 32. The method of claim 30, wherein the radiolabel comprises a member selected from the group consisting of ²²⁵Ac, ¹⁷⁷Lu, ²¹¹At, ¹³¹I, ¹²⁴I, ⁹⁰Y, ⁶⁴Cu, ⁶⁸Ga, ⁶⁶Ga, and ⁶⁷Cu.
 33. The method of claim 29, comprising displaying a representation image of an antibody-antigen interaction of the pre-therapy tracer of the mAb with tissue.
 34. The method of claim 33, wherein the antibody-antigen interaction is determined by measuring targeting antibody affinity or antigen density, or internalization rates of antibody-antigen complex.
 35. The method of claim 1, wherein the nonlinear compartmental model is as depicted in FIG.
 2. 36. The method of claim 29, wherein the administering step comprises administering a monoclonal antibody (mAb) with a first cargo and a second cargo, the method further comprising estimating, by the processor, a residence time of the first and second antibody cargos transported and bound with the same type of mAb.
 37. The method of claim 1, further comprising administering the therapeutic antibody dose to the subject.
 38. The method of claim 1, wherein the administration is via injection.
 39. The method of claim 1, wherein the one or more parameters comprises a therapeutic index.
 40. The method of claim 39, wherein the therapeutic index comprises a PET image-derived tumor-to-normal tissue ratio.
 41. The method of claim 40, wherein the tumor-to-normal tissue comprises a member selected from the group consisting of tumor-to-bowel mucosa, tumor-to-breast, tumor-to-prostate, tumor-to-lung, tumor-to-kidney, tumor-to- liver, tumor-to- salivary gland, tumor-to-bone marrow, tumor-to-liver, tumor-to-lung, tumor-to-kidney, tumor-to-bowel, tumor-to-bladder, tumor-to-gonads, and tumor-to-brain.
 42. A method of determining a patient-specific optimum therapeutic mAb dose, the method comprising: receiving, by a processor of a computing device, a plurality of sets of images acquired over a period of time, wherein each set of images is acquired at a particular time following administration of the radiolabeled monoclonal antibody as pre-therapy tracer, and wherein each set of images visualizes a blood pool, tumor, and antigen-positive normal tissue; determining, by the processor, from the plurality of sets of images, measurements of time-activity of the pre-therapy tracer of the mAb in blood, tumor, and antigen-expressing normal tissue for the subject; identifying, by the processor, one or more parameters of a nonlinear compartmental model of a time-varying biodistribution of the pre-therapy tracer in the subject from the determined measurements of time-activity for the subject, wherein the one or more parameters comprises a targeting index that reflects a relative amount of the pre-therapy tracer delivered to the tumor and wherein the targeting index comprises a tumor-to-plasma ratio of integrals of tumor and plasma activity concentrations, wherein the nonlinear compartmental model comprises a system of differential equations expressing rate of change in the amount of pre-therapy tracer in each of three compartments comprising a plasma compartment, a normal tissue compartment, and a tumor tissue compartment, wherein model nonlinearity is introduced by: expressing flux of the pre-therapy tracer from plasma to normal tissue using a first time-varying parameter proportional to a first concentration, wherein the first concentration is the difference between a total antigen concentration in the normal tissue compartment and unbound pre-therapy tracer concentration in the normal tissue compartment which varies with time and expressing flux of the pre-therapy tracer from plasma to tumor tissue using a time-varying parameter proportional to a second concentration, wherein the second concentration is the difference between a total antigen concentration in the tumor tissue compartment and unbound pre-therapy tracer concentration in the tumor tissue compartment which varies with time, and wherein evaluation of the one or more parameters for the subject is performed by fitting the nonlinear compartmental model to the subject's measured time-activity data; and determining, by the processor, a patient-specific optimum therapeutic antibody dose for the subject using the nonlinear compartmental model with the identified one or more parameters, wherein the patient-specific optimum therapeutic antibody dose is that which yields the maximum tumor-to-normal-tissue cumulated activity concentration ratio.
 43. The method of claim 42, further comprising acquiring the plurality of sets of images via positron emission tomography (PET).
 44. The method of claim 42, wherein a first set of the images is acquired several hours after post-antibody administration and at least a second set of the images is acquired up to 2 weeks after antibody administration.
 45. The method of claim 44, wherein the first set of images is acquired at least 2 hours, at least 3 hours, at least 4 hours, or at least 5 hours after post-antibody administration.
 46. The method of claim 42, wherein the antigen-positive normal tissue comprises colon tissue.
 47. The method of claim 42, wherein the antigen-positive normal tissue comprises breast tissue.
 48. The method of claim 42, wherein the antigen-positive normal tissue comprises prostate tissue.
 49. The method of claim 42, wherein the therapeutic antibody is the same monoclonal antibody as in the pre-therapy tracer.
 50. The method of claim 42, wherein the therapeutic antibody is different than the monoclonal antibody in the pre-therapy tracer.
 51. The method of claim 42, wherein the one or more parameters comprise a targeting index.
 52. The method of claim 1, wherein the administering step comprises: administering a non-radiolabeled mAb, and administering a radiolabeled reagent, wherein the radiolabeled reagent reacts with the non-radiolabeled mAb.
 53. The method of claim 52, wherein the radiolabeled reagent reacts with the pre-therapy tracer. 